Abstract
Bundle adjustment constitutes a large, nonlinear least-squares problem that is often solved as the last step of feature-based structure and motion estimation computer vision algorithms to obtain optimal estimates. Due to the very large number of parameters involved, a general purpose least-squares algorithm incurs high computational and memory storage costs when applied to bundle adjustment. Fortunately, the lack of interaction among certain subgroups of parameters results in the corresponding Jacobian being sparse, a fact that can be exploited to achieve considerable computational savings. This article presents sba, a publicly available C/C++ software package for realizing generic bundle adjustment with high…
Citation impact
771
total citations
- FWCI
- 60.73
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- 100%
- References
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Authors
2Topics & keywords
Topics
Keywords
- Jacobian matrix and determinant
- Bundle
- Bundle adjustment
- Computer science
- Flexibility (engineering)
- Feature (linguistics)
- Least-squares function approximation
- Mathematical optimization
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